This paper provides a workflow to automate the application of multi-segment Arps decline model to forecast production in unconventional reservoirs. Due to significant activity in the shale plays, a single reservoir engineer may be tasked with managing hundreds of wells. In such cases, production forecasting using a multi-segment Arps model for all individual wells can be a challenging and time-consuming process. Although popular industry software provide some relief, each approach has its individual limitations. We present a workflow to automate the application of multi-segmented Arps decline model for easier and more accurate production forecasting using suitable statistical and machine learning methods.

We start by removing outliers from our rate normalized pressure (RNP) data using angle-based outlier detection (ABOD) technique. This technique helps us clean our production data objectively to improve production forecasting and rate transient analysis (RTA). Next, we correct the non-monotonic behavior of material balance time (MBT) and smooth the RNP data using a constrained generalized additive model. We follow it by using the Ramer–Douglas–Peucker (RDP) algorithm as a change-point detection technique to automate the flow regime identification process. Finally, we calculate a b-value for each identified flow regime and forecast future production. We demonstrate the complete workflow using a field example from shale play.

The presented workflow effectively and efficiently automates the rate transient analysis work and production forecasting using multi-segment Arps decline model. This results in more accurate production forecasts and greatly enhanced work productivity.

The workflow presented, based on selected algorithms from statistics and machine-learning, automates multi-segment Arp’s decline curve analysis, and it can be used to forecast production for a large number of unconventional wells in a simple and time efficient manner.

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